1. Field of the Invention
The present invention relates to an image compression apparatus, an image compression program, and an image compression method that perform lossless compression of a 3D image.
2. Description of the Related Art
Performance of an imaging diagnostic apparatus such as a CT (Computed Tomography) or MR (Magnetic Resonance) has recently been improved. This allows, for example, imaging of the cross-section of a human body with a slice interval of as small as less than 1 mm. With such an imaging diagnostic apparatus, a multislice image constituted by a plurality of frames can be obtained as a scanned image per one inspection. The multislice image is obtained by sequentially scanning a cross-section image (frame) while changing the coordinate of the axis perpendicular to the cross-section image for each slice interval. When the slice interval is decreased, the number of frames of the multislice image that can be obtained per one inspection is increased up to several hundred to thousand, which corresponds to several hundred MB (Byte) in terms of data capacity.
The scanned multislice images are stored in an image server installed in a computer room of a medical institution and are transferred to a client terminal via a network at reading time or physical examination time for display or reference. Medical images that have been used in physical examination are legally required to be kept for a long period of time, so that the amount of image data to be stored is increasing yearly. Therefore, compression of the medical image is essential for increasing the storage amount of the data in an image server. Further, there is a strong demand that the scanned medical image is stored without degradation and, therefore, a lossless (reversible) compression method has often been used.
Examples of a conventional lossless image compression method applied to the medical image include lossless JPEG (Joint Photographic Experts Group), JPEG-LS (Lossless), JPEG 2000, and the like. Presently, Lossless JPEG is used most widely, and JPEG 2000 is used partly.
Lossless JPEG uses one to three pixels selected from among three neighboring pixels of a target pixel to calculate a prediction value of the target pixel and applies Huffman coding to a difference between the calculated prediction value and the true value of the target pixel. Seven prediction methods are defined and one predictor is selected for each image. The compression ratio, which depends on the type of image, is about 1/2 to 1/3 in the case of the medical image.
JPEG 2000 applies a discrete wavelet transform (DWT) to an image to transform it into frequency space and performs modeling for the transformed data so as to increase entropy coding efficiency, followed by arithmetic coding process. The compression ratio of JPEG 2000 for a medical image is about 1/3 to 1/4 which is higher than that of lossless JPEG.
JPEG-LS calculates a prediction value of a target pixel from four neighboring pixels of the target pixel and applies Golomb-Rice coding to the prediction error. A prediction method is selected from among three prediction methods for each pixel depending on the value of the target pixel. The compression ratio of JPEG-LS for a medical image is comparable with that of JPEG 2000.
The abovementioned lossless image compression method is an algorithm that performs compression on a per image basis. Nowadays, as a compression method for a medical image, such as a multislice CT/MR image, in which there is a correlation between frames, a method that uses a 3D DWT has been proposed. This method achieves a higher compression ratio than that of the abovementioned method that performs compression on a per image basis.
As a prior art related to the present invention, there is known an image data compression method that compresses a frame image constituting a plurality of slices of tomographic image or moving image (refer to, for example, Patent Document 1: Jpn. Pat. Appln. Laid-Open Publication No. 2005-245922).
The multislice CT/MR image, which is an image obtained by using a sensor provided outside a human body to detect the dosage of X-ray radiation transmitted through a human body or radiation dose from a radioactive substance administered into a human body and reconstructing, e.g., a distribution of X-ray absorption of the human body across a measured cross-section so as to visualize the condition of tissues inside the body, differs largely from a natural image that is obtained by directly photographing a subject.
In particular, the multislice CT/MR image may include noise caused by the sensor and reconstruction process, which does not exist in the natural image in general. Radiated noise caused in multislice CT image is a conspicuous example of the noise.
In the case where a conventional image compression method is used to compress the medical image including such noise that does not exist in a natural image, the compression efficiency becomes lower as compared with the case of the natural image, thus making it difficult to achieve a high image compression ratio.
The natural image has locally a high continuity between pixel values. The above-mentioned problem of difficulty in achieving a high image compression ratio is caused by the fact that the conventional image compression method is based on this feature of the natural image. When the DWT is applied to the natural image, the distribution of pixel values tends to be biased toward low frequency components of frequency space in the transformed data and, as this trend becomes stronger, the compression ratio becomes higher. On the other hand, the medical image including noise contains more high frequency components than the natural image does, so that the degree of the bias in the frequency space becomes small than that in the natural image, resulting in a decrease in a compression ratio.
Further, since the medical image needs to be compressed without image quality degradation, i.e., in a lossless fashion as described above, image processing involving the image quality degradation, such as one that quantizes frequency components to reduce information amount, cannot be applied to the medical image. This makes it difficult to achieve a higher compression ratio of the medical image.
As described above, in view of the sharp increase in the number of multislice images, i.e., the data amount thereof due to the technological advancement in an imaging diagnostic apparatus, there has been a need to compress the multislice image at a higher compression ratio in a lossless fashion.